Wetland soil characteristics influence the kinetics of dissolved organic carbon sorption

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Morrissette, Patrick J. Neale, J. Patrick Megonigal, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3813404/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jul, 2024 Read the published version in Wetlands → Version 1 posted 5 You are reading this latest preprint version Abstract Sorption processes at the soil-water interface are observed to be rapid and dominant pathways of dissolved organic carbon (DOC) exchange. However, kinetics data for sorption are sparse, and non-existent for temperate tidal marshes. In this study, sorption rate kinetics experiments were designed to constrain new formulations of a sediment flux model coded to include explicit sorption between soil organic carbon and DOC pools. Batch incubations for marsh soil samples from Taskinas Creek (VA, USA) and Jug Bay Wetlands Sanctuary (MD, USA) were performed anaerobically under four sets of initial conditions: permutations of two salinities (0 psu, 35 psu) and two DOC concentrations (0 mg L -1 , 275 mg L -1 ). Rates were measured at seven time points over 24 hours. These results are the first DOC sorption kinetics data for tidal marsh soils, revealing that 76% of total sorption occurred within 15 minutes. The results also revealed higher capacity for adsorption under high DOC concentrations and salinity, and vice versa, with differences in magnitude between soil types. Numerical models simulating processes from these experiments provided a range of rates by fitting linear first order and non-linear ordinary differential equations to the kinetic change in DOC concentration curves over time. The output suggested that introducing a saturation coefficient improved model fits across all cases. These results provide a deeper understanding of the biogeochemical controls on sorption kinetics and suggest that it is crucial to incorporate sorption processes into sediment flux models to accurately represent DOC fluxes from tidal marshes. sorption kinetics dissolved organic matter tidal marsh Langmuir model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Wetlands are among the most productive ecosystems on earth, and they exert a strong influence on dissolved organic carbon (DOC) export to aquatic ecosystems. Tidal marshes are important sources of DOC to estuaries and coastal waters (Tzortziou et al., 2011 ; Herrmann et al., 2015 ; Najjar et al., 2018 ), resulting in as much as 80% of the annual lateral DOC and dissolved inorganic carbon (DIC) export to oceans (Wang & Cai, 2004 ; Wang et al., 2016 ). Despite occupying less than 5 percent of the total land surface, tidal wetlands disproportionately contribute to coastal productivity, carbon fluxes, and soil carbon accumulation (Pendleton et al., 2012 ; Najjar et al., 2018 ; Windham-Myers et al., 2018 ). Quantifying the coastal carbon flux is challenging due to the extensive spatial and temporal heterogeneity across interacting coastal ecosystems, but necessary due to the importance of coastal ecosystems in the global carbon cycle. Models such as the Sediment Flux Model (SFM; Di Toro, 2001 ; Brady et al., 2013 ; Testa et al., 2013 ) attempt to accurately simulate observed nutrient and carbon exchange and transformation within coastal soils and dissolved organic carbon was recently added to the SFM (Clark et al., 2017 ). This was done to address the gap between the increasing number of studies that show that DOC is a large and important component of the coastal carbon budget, and the fact that DOC is rarely addressed in sediment-water column flux models (Burdige et al., 2016 ; Clark et al., 2017 ). Sorption is an important process to quantify in sediment-water column flux models because it regulates the partitioning of DOC between water and soil surfaces. Many studies in a variety of ecosystems have demonstrated that soils and sediment can readily adsorb DOC, particularly those dominated by poorly crystalline iron and aluminum oxides with high surface area and sorption capacity (Kothawala et al., 2008 ; Shaker et al., 2012 ; Pinsonneault et al., 2021 ). Isotherms are typically used to quantify the sorption characteristics of soils and sediments. This approach involves adding DOC of a known initial concentration to soils and allowing time for simultaneous sorption and desorption processes to reach thermodynamic equilibrium (Qualls & Richardson, 2003 ). Equilibrium isotherms have proven valuable for understanding the amount of DOC that soils adsorb or desorb as a function of surface properties, environmental conditions, and other physicochemical factors (Vandenbruwane et al., 2007 ). However, they do not quantify rates of DOC sorption and desorption that are critical for parameterizing sediment-water column flux models. Laboratory incubations and in situ measurements suggest that adsorption and desorption are rapid processes (Kaiser & Zech, 1998 ; Kothawala et al., 2008 ; Shaker et al., 2012 ). In 24-hour batch sorption experiments where DOC-free solution was added to forest soils, the authors reported up to 75% of the sorption processes happening in the first 15 minutes, and 90% occurring by the end of 4 hours (Kaiser & Zech, 1998 ). Another study, where sorption onto mineral soils was tested under varying levels of pH and other conditions, found that equilibrium occurred in less than 30 minutes regardless of initial conditions (Shaker et al., 2012 ). Other kinetic studies indicate that adsorption and desorption are time-dependent and competitive, with adsorption rates slowing over time and sometimes inducing desorption depending on the compounds (Lilienfein et al., 2004 ; Koopal et al., 2019 ). To our knowledge, however, there has been only one equilibrium isotherm study with DOC from coastal wetlands soils and soils (Pinsonneault et al., 2021 ) and no studies of DOC sorption-desorption kinetics. Incorporating coastal wetland soils into DOC transport experiments is vital due to the influence of salinity on its exchange. Salinity has been shown to have an impact on DOC equilibrium, as changes in ionic strength lead to competition at exchange sites on the soil surface (Seitzinger et al., 1991 ; Stumm & Morgan, 2012 ). Tidal marshes range in salinity from freshwater to fully saline sea water and are susceptible to changes in salinity driven by precipitation, sea level rise, storm flooding, and human activity. It has also been shown that salinity impacts sorption capacity and binding affinity (Pinsonneault et al., 2021 ), which inherently affects the speed at which sorption processes can occur. We hypothesized that DOC export from tidal marshes varies with soil type and salinity, and that it was possible to capture this variability in DOM transport models. While previous isotherm experiments on coastal wetland soils were instructive as to how soil composition affected net DOC exchange (Pinsonneault et al., 2021 ), they did not answer three questions that are key to modeling: 1) how fast are the sorption processes? 2) do the sorption rates vary as a function of time? and 3) how much time is needed to reach steady state? We addressed the lack of DOC sorption-desorption kinetics data in aquatic ecosystems generally and the absence of such data for tidal wetlands specifically by quantifying rates for multiple marsh soil types and initial conditions in closed laboratory soil incubations. We then formulated a suite of simplified numerical sorption models to simulate the zero-dimensional conditions in these kinetic sorption experiments, providing insights into the dynamics of the sorption processes over time and rate parameters that are needed to include sorption processes in more complex sediment flux models like the SFM. Methods Kinetic isotherm experiments Study sites Soils were collected from two of the four tidal marshes in the Chesapeake Bay estuary along the Northeast US that were the subject of isotherm studies in Pinsonneault et al. ( 2021 ): (1) Kirkpatrick Marsh in Edgewater, Maryland, U.S.A. (36°53’N, 76°33’W), (2) Jug Bay Wetlands Sanctuary in Lothian, Maryland, U.S.A. (38°46’N, 76°42’W), (3) Taskinas Marsh near Williamsburg, Virginia, U.S.A. (37°25’N, 76°43’W), and (4) Wachapreague Marsh in Wachapreague, Virginia, U.S.A. (37°32’N, 75°41’W) (Fig. 1 ). These four systems capture a wide range of marsh characteristics such as salinity, soil organic matter (SOM), and plant biodiversity (Pondell & Canuel, 2022 ). The variability encompassed by these sites provided insights into the soil characteristics that are critical to formulate and parameterize numerical models. A detailed description of all four sites can be found in Pinsonneault et al. ( 2021 ) and Pondell & Canuel ( 2022 ). For this study, we chose two sites that bracket the full range of soil exchange characteristics of the four sites as reported by Pinsonneault et al. ( 2021 ): Jug Bay, which had the highest equilibrium net-adsorption at high salinity, and Taskinas, which had the highest net-desorption under freshwater conditions (0 salinity). Experimental design Soils were subjected to two end-member salinity treatments (0 psu, 35 psu) and two end-member DOC treatments (0 mg L -1 , 275 mg L -1 ). Rates were measured at seven time points during those 24 hours, with the first four time points taken at short intervals over the first hour to capture potentially rapid sorption processes (Kaiser & Zech, 1998 ; Shaker et al., 2012 ). Stock creation Following the protocol set forth in Pinsonneault et al. ( 2021 ), surface water was collected in August 2018 from the Jericho Ditch of the Great Dismal Swamp Wildlife Refuge, VA (36°41’45.03”N, 76°30’28.16”W) (Fig. 1 ) to create a concentrated DOC stock inoculant. 120 L of highly colored water was concentrated via reverse osmosis, filtered through 0.2 µm to remove particulates, and stabilized with sodium azide (NaN 3 ; 1 µM) to eliminate microbial activity. This stock solution was the fresh high-DOC initial concentration. Instant Ocean was added to a sub-volume to reach 35 psu salinity and pH, salinity, and conductivity were measured for both solutions. A parallel low-DOC stock was prepared by adding an equivalent quantity of NaN 3 to the equivalent volume of DI water. Hydrochloric acid (HCl) was added until the pH matched the concentrated stock solution, then Instant Ocean was added to a subset of the dilutant until the salinity and conductivity matched the high-salinity concentrated DOC stock solution. This procedure produced four stock solutions: high DOC + no salt (HF), high DOC + salt (HS), low DOC + no salt (LF), and low DOC + salt (LS). Optical properties and total organic carbon concentration were measured for all final stock solutions, as well as throughout the preparation process to ascertain the effects of each preparation step on the DOC quantity and quality. Absorbance did not differ significantly throughout the process ( p = 0.989). Slopes of CDOM absorption spectra over the wavelength ranges of 275 to 295 nm (S 275-295 ) and 350 to 400 nm (S 350-400 ), among other indicators of DOC composition, only varied by a standard deviation of ± 1.3% (S 275-295 ) and ± 1.8% (S 350-400 ) across all five iterations of retentate. From this, it was concluded that the compounds within the stock solution remained stable in source and chemical make-up. Concentrations did not differ significantly (p = 0.187) between steps except for post-reverse osmosis when the DOC concentration was purposefully increased by three-fold. Experimental procedure The experimental procedure and pre- and post-analyses followed Pinsonneault et al. ( 2021 ), using the same cored, dried, and homogenized soils from the isotherm experiments and stored properly in a desiccator between incubations. The aqueous phase of each soil slurry was measured for total organic carbon concentration and spectral properties. The soil slurry itself had a volume to mass ration of 30 mL of solution to 1 g of soil. These included Excitation-Emission Matrices (EEMs), Humification Index (HIX), Freshness Index (β:α), Biological Index (BIX), and Fluorescence Index (FI) on a FluoroMax 3 spectrofluorometer for information on the composition of the DOC compounds (as per Helms et al., 2008 ; Hansen et al., 2016 ). Absorbance was measured on a Thermoscientific spectrophotometer, and total organic carbon (TOC) was measured on a Shimadzu TOC-L for the quantity of DOC throughout the incubations. Optical properties were measured to determine the quality of the DOC and how it shifted through time due to sorption, providing information on molecular weight, source, and partitioning between colored and non-colored fractions. The detailed methods and results of these measurements can be found in (Morrissette, 2021 ; Pinsonneault et al., 2021 ). Kinetic models Parameterization The subsequent sections describe the formulation and parameterization of a suite of carbon-based zero-dimensional models that were developed to simulate the laboratory kinetic sorption incubations detailed above. The model formulations assumed that the organic carbon exchanges in the laboratory incubations were driven by adsorption and desorption processes. However, it should be noted that these incubations used material from field-moist cores that were freeze-dried before preparation of soil fractions. Thus, any DOC that was present in the water fraction of the core may have contributed to DOC exchange upon re-wetting of the soil due to the re-dissolution of this residual DOC fraction. This potential contribution was conservatively estimated by assuming any water present in the core at the time of extraction contained DOC at the pore water concentration, and all of that DOC was re-dissolved into the incubation solution. Water content was obtained by oven drying the cores and DOC was measured in pore water sampled from soil "peepers" (A. J. Pinsonneault unpubl .). Optical analyses described in Morrissette ( 2021 ) and Neale et al. ( 2023 ) showed that multiple pools of organic carbon were involved in the exchange. However, only the bulk DOC pool, soil-associated organic carbon (Cs), and solution-associated DOC were modeled in this study. Figure 2 shows a simplified conceptual model where at time 0 (Fig. 2 a), Cs and DOC were separated, followed by exchange over time with sorption of the bulk DOC (Fig. 2 b). Once DOC was adsorbed to the soil, it was considered a part of the Cs pool, and vice versa. The experiments were modeled mathematically using two alternative sets of differential equations that simulated the rate of DOC movement in the closed experiments due to adsorption and desorption. The simplest were Linear equations (Eqs. 1 & 2) solved analytically, whereas the more complex were Langmuir equations (Eqs. 5 & 6) solved numerically. In both cases, equation parameters that provided the best fit to the experimental results were determined by least-squares minimization (non-linear regression). A third Time-dependent model was formulated and coded to explicitly capture the rapid, initial oscillations in DOC sorption by allowing the desorption parameter to change over time. It is only briefly discussed here. See Morrissette ( 2021 ) and the supplementary material for the full equations, parameter descriptions, and results. Linear model Equations Assuming first order ordinary differential equations (ODEs) for the exchange described above (Fig. 2 ), a mathematical representation of the sorption experiments was formulated as follows: (1) \(\frac{dCs}{dt}\) = \(-{k}_{des}\times Cs + {k}_{ads}\times DOC\) (2) \(\frac{dDOC}{dt}\) = \({k}_{des}\times Cs - {k}_{ads}\times DOC\) where each equation represents the change in organic carbon mass over time in either the solution (DOC) or soil (Cs). Both pools have an associated rate parameter; desorption (k des ) associated with the soil pool, and adsorption (k ads ) associated with the solution pool. The solution pool loses mass due to adsorption (leaving solution to attach to soils) and gains mass from desorption (loss from soils to solution), whereas for the soil pool the opposite is true. This is termed the Linear model since exchanges are a linear function of the mass in each pool. Equations were mass-specific since the mass of soil (1 g) and volume of solution (30 mL) were constant throughout the experiments. Parameter descriptions and units can be found in Table 1 . Analytical solution and fitting Equations 1 & 2 are linear first order ODEs with constant coefficients (k des ; k ads ). Following a well-known process for solving a homogeneous system through substitution of the unknowns and finding the roots of the equation (Herman, 2018), a generic solution for each equation was calculated, solved, and simplified to be: $$Cs\left(t\right)= \frac{1}{{k}_{ads}+{k}_{des}}\left[Cs\left(0\right)\left({k}_{ads}+{k}_{des}{e}^{-\left({k}_{ads}+{k}_{des}\right)t}\right) + DOC\left(0\right){k}_{ads}\left(1- {e}^{-\left({k}_{ads}+{k}_{des}\right)t}\right)\right]$$ (3) $$DOC\left(t\right)= \frac{1}{{k}_{ads}+{k}_{des}}[Cs\left(0\right)kdes\left(1- {e}^{-\left({k}_{ads}+{k}_{des}\right)t}\right)+DOC\left(0\right)\left({k}_{des}+{k}_{ads} {e}^{-\left({k}_{ads}+{k}_{des}\right)t}\right)]$$ (4) Using the “fitnlm” function in Matlab 2020a, time (t) was provided along with values for DOC(0) and Cs(0). Cs at time 0 was determined from the native desorbable carbon (C 0 ) pool calculations, described in Pinsonneault et al. ( 2021 ). Initial estimates for k ads and k des were passed to the function, which used the input parameters to fit a curve to the observational DOC data and estimate best parameter values for k ads and k des as the relative unknowns. Initial data points that created a sorption “spike” were omitted during the fitting process, as the Linear model could not capture the directional shift. When analyzing the sorption isotherms from Pinsonneault et al. ( 2021 ), it is apparent that regardless of the initial concentration of DOC in solution, the range of isotherm values per salinity level could be fit with one curve from one isotherm equation. This indicates that, for this kinetic model, one set of k ads and k des parameters should be able to be applied to both the high [DOC] and low [DOC] kinetic results within one site. However, k ads and k des should differ between the fresh and saline initial conditions since the isotherms vary with salinity level. Thus, for each site we obtained one set of best fit coefficients for each set of combined data from incubations with high [DOC] and low [DOC] initial conditions. Langmuir model Based on the isotherm curves from Pinsonneault et al. ( 2021 ), saturation of adsorption could be expected for sufficiently high DOC, especially at lower salinity. Since the stock inoculant for the kinetics incubations reported here had an even higher concentration of DOC than used by Pinsonneault et al. ( 2021 ), adsorption saturation on the Taskinas and Jug Bay soils theoretically could have occurred. A version of the model was therefore formulated that included a saturation coefficient that allowed sorption to slow or stop when approaching the limit of available adsorption sites over time. Parameter descriptions can be found in Table 1 . Equations Saturation was introduced into equations 1 & 2 as follows: (5) \(\frac{dCs}{dt}\) = \(-{k}_{des}\times Cs +{{k}_{ads}}_{2}\times DOC\times S\) (6) \(\frac{dDOC}{dt}\) = \({k}_{des}\times Cs-{{k}_{ads}}_{2}\times DOC\times S\) where k ads2 is a second-order rate constant with units of mg − 1 hr − 1 , and the adsorption terms were multiplied by the saturation coefficient (S; mg). S was calculated as: (7) \(S = Qmax - Cs\) where Q max (mg) is the maximum adsorption capacity for the soil and Cs (mg) is the mass on the soil. Adding the saturation coefficient, assuming a monolayer adsorption scheme, controls the rate of adsorption based on the adsorption capacity of the soils. If Q max increases or Cs decreases, the rate of adsorption could increase due to a larger S, and if the opposite occurs, the rate of adsorption could slow or stop. Solving Eq. 6 for equilibrium DOC (DOC'=0) given initial values of soil OC (C 0 ) and DOC yields the Langmuir isotherm (Eq. 8, see below, K = k ads2 /k des ). As with the linear model, initial rapid exchanges of DOC that often occurred in the experiments could not be simulated by this Langmuir model, so these initial data points were omitted during fitting. Also, volume (of solution) and mass (of soil) are not included in the equations as they were constant throughout the experiments. If being used in generalized models, the equations and values would have the units of volume and mass added back in for unit uniformity, e.g. k ads2 would have units of L mg − 1 hr − 1 (cf. Table 1 ). Equations in generalized format can be found in the supplementary file (Table A.4). Initial values Since the soil samples used in the incubations were the same as Pinsonneault et al. ( 2021 ), fixed parameter values for Q max and Cs at time 0 were used from their fitted Langmuir isotherms. DOC at time 0 was always set to be the pre-incubation observed DOC value from laboratory analysis. Initial values for all the simulations can be found in supplementary (Appendix A). The fitting scheme, using “ode23” in Matlab 2020a, performed best-fit analysis as in the linear model, simultaneously analyzing high and low [DOC] data sets for one set of rate coefficients. Table 1 Model parameters, descriptions, and units. Values of each parameter for time 0 were scenario-dependent (Appendix A). Parameter Description Unit Model Version k des Desorption rate coefficient hr − 1 Linear, Langmuir k ads Adsorption rate coefficient hr − 1 Linear Cs(t) Mass of organic carbon adsorbed on soils mg Linear DOC(t) Mass of DOC free in solution mg Linear S Saturation coefficient mg Langmuir Q max Maximum adsorption capacity mg Langmuir k ads2 Saturation adsorption rate coefficient mg − 1 hr − 1 Langmuir Analysis Two-sample t-tests (assuming equal variances) were used to test the difference between sites for each initial condition and the percent of sorption completed over time and to compare the effects of salinity and DOC concentration within one site. Confidence intervals for the original isotherm curves were calculated via error propagation of the statistical uncertainty in the fitted parameters of the (non-linear) Langmuir isotherm equation: (8) \({\varDelta \left[DOC\right]}_{0-f} = {\frac{(Qmax \times K \times [DOC\left]f\right)}{(1 + (K \times \left[DOC\right]f\left)\right)}}_{}\) - \(C0\) where [DOC] 0-f is the change in DOC concentration subtracting final values from initial, Q max is the maximum sorption capacity, K is the binding affinity, [DOC] f is the final DOC concentration in solution, and C 0 is the amount of desorbable organic carbon on the wetland soils. Four sets of data from the Langmuir isotherm experiments (Pinsonneault et al., 2021 ) were chosen to be directly compared to the kinetic data: Jug Bay Fresh (JBF, 0 psu), Jug Bay Saline (JBS, 35 psu), Taskinas Fresh (TAF, 0 psu), and Taskinas Saline (TAS, 35 psu). The full range of [DOC] values for each of the four were plotted, then the matching 8 points chosen from relevant kinetic data – the low and high DOC concentrations of JBF, JBS, TAF, and TAS – were graphed onto those curves. For the high [DOC] data points, the kinetic points were checked against the original Langmuir isotherm curve confidence intervals. For TAS, the confidence interval was not included. Since it was more linear than the others and did not indicate any approach in saturation, the non-linear fit was not able to obtain an acceptable value of Q max . Instead, the kinetic point was compared to an isotherm that was estimated using an assumed Q max . The best-fit parameters for the linear and Langmuir model were estimated in Matlab 2020a using the “nlmfit” function. The Langmuir numerical model was coded and analyzed in Matlab 2020a using the “ode23” function. Additional packages within RStudio of “dplyr”, “tidyr”, “patchwork”, and “ggplot2” were used for data manipulation and graphing. A GitHub repository “Kinetic-Sorption-Incubation-Models” with the model code and input files is available here: https://https://github.com/hannah-morrissette/Kinetic-Sorption-Incubation-Models . Root mean square error (RMSE), model efficiency (MEF; Stow et al., 2009 ), average absolute error (AAE), adjusted R 2 , and Spearman rank correlation values were calculated to assess model performance. RMSE is shown on the graphs in the results section and ranked tables can be found in the supplementary information (Table B.1). As described previously, “peaks” in the data were omitted for the linear and Langmuir models before calculating these model performance metrics. Results Kinetic incubations The two tidal wetland soils subjected to four sets of initial conditions (HF, HS, LF, LS) generated both net adsorption (negative Δ[DOC] (y-axis)) and net desorption (positive Δ[DOC] (y-axis)) patterns (Fig. 3 ). For Jug Bay soils (Fig. 3 a), 77.8 ± 0.1% of the net exchange occurred within 15 minutes. Net adsorption occurred when initial [DOC] was high (HF and HS incubations), while net desorption occurred when initial [DOC] was low (LF and LS treatments), with no significant difference between LF and LS ( p = 0.967, two-sample t-test). In contrast, there was a doubling of the amount of adsorption that occurred between the HF and HS treatments, revealing that the effects of salinity on sorption were amplified under the high [DOC] conditions. The four incubations reached equilibrium at a Δ[DOC] of -53.8 ± 4.3 mg L − 1 (HF), -124.2 ± 0.6 mg L − 1 (HS), 34.1 ± 1.8 mg L − 1 (LF), and 34.0 ± 1.6 mg L − 1 (LS). Taskinas soils followed similar patterns in sorption processes with time (Fig. 3 b), with an average of 74.7 ± 0.1% of sorption completed within 15 minutes, reaching relative equilibrium before 6 hours. However, in contrast to the Jug Bay soil, there was net desorption in the HF treatment such that the only treatment with net adsorption was HS. The magnitude of adsorption in the HS treatment was much lower than in the HF treatment, but the absolute difference between the two was similar to the difference in the same treatments for Jug Bay. LF and LS were significantly different for Taskinas ( p = 2.76 x 10 − 7 , two-sample t-test), indicating a stronger response to the initial conditions. Equilibrium values for the change in [DOC] were 25.9 ± 0.5 mg L − 1 (HF), -47.2 ± 3.9 mg L − 1 (HS), 50.3 ± 1.7 mg L − 1 (LF), and 33.5 ± 0.2 mg L − 1 (LS). On average, 76.2 ± 0.1% and 83.5 ± 0.1% of the total sorption, pooled across both Jug Bay and Taskinas soils, was completed in the first 15 minutes and 1 hour, respectively, with 93.3 ± 0.0% of sorption at 6 hours (Fig. 4 ). The results confirm that 24 hours was sufficient time for sorption isotherm incubations of these wetland soils. Taskinas processes took slightly more time to reach equilibrium than Jug Bay based on percent completion of total exchange for each time point, but the difference was not statistically significant. Kinetic model solutions For brevity, the Linear and Langmuir solutions are reported and discussed for only the four sets of initial conditions (HF, HS, LF, and LS) for the Jug Bay experiments described above. These results can be taken as representative of all the model fits to the experiments described in this manuscript (see supplemental appendices C-F) and samples from other sites (Morrissette, 2021 ). Linear solution The fits to Jug Bay kinetic data followed either exponential decay or saturation functions depending on the parameter values. When DOC was initially high the experiment was dominated by adsorption over time and the non-linear regression fits to the analytical solution of the linear model captured this decay, while the opposite was true when DOC was initially low. In most cases the regressions that used a common set of k ads and k des for high and low DOC provided poor fits to the observed data, signifying that the simplest linear equations alone cannot capture the sorption incubation results (Fig. 5 ). High salinity affected the regression fit the most, as the converged set of parameters still resulted in substantial overestimation of DOC over time. Langmuir solution Introducing a saturation coefficient to the ODEs improved the model fits across all cases, especially for the high salinity incubations (Fig. 6 ). Occasionally, the inflection points of the curves sharpened slightly compared to the Linear model. The fits for the Taskinas data (Appendix D) depicted a similar pattern of a major improvement over the fits to the linear model, suggesting that saturation is an important parameter and should be included in the model equations to improve the fits to the observed data. Comparison of experimental results To increase reliability in the presented data, and demonstrate further consistency with the isotherms, two comparisons between the kinetic data and isotherms were made. First, we related the kinetic steady state ΔDOC with the final isotherm values (Fig. 7 ). Four sets of data from the Langmuir isotherm experiments (Pinsonneault et al., 2021 ) were directly compared to the kinetic data: Jug Bay Fresh (JBF, 0 psu), Jug Bay Saline (JBS, 35 psu), Taskinas Fresh (TAF, 0 psu), and Taskinas Saline (TAS, 35 psu). For the low [DOC] values, there were no differences between the isotherm and kinetic data points (p = 0.995, two-sample t-test). For the high [DOC] data points, the kinetic experiments’ concentrated stock solution was, on average, 88.8 mg-DOC L − 1 higher than the stock solution for the high initial [DOC] conditions of the isotherm experiments, but extrapolated confidence intervals captured the net DOC exchange of the kinetic points. Second, we compared the Langmuir k ads2 :k des ratio, an estimate of binding affinity, with the binding affinities (K) reported in Pinsonneault et al. ( 2021 ). The difference between the kinetic and isotherm data for any treatments are within the standard errors (Table 2 ), showing a high degree of reproducibility of the adsorption-desorption characteristics of these marsh soils despite differing experimental and analytical approaches. Table 2 Comparison of binding affinities (L mg − 1 ) of the Langmuir isotherm as obtained from fits to equilibrium results (Pinsonneault et al. 2021 ) and inferred from the fits to kinetic data. Value JBF JBS TAF TAS k ads2 :k des (kinetic) 0.0423 0.0116 0.0083 0.0021 Binding affinity (isotherm) 0.035 ± 0.010 0.010 ± 0.002 0.006 ± 0.003 0.002 ± 0.000 Discussion Kinetic isotherms Sorption kinetics in tidal marsh soils can vary dramatically between sites and for different initial conditions as demonstrated by the striking differences reported here for soils from two sites in Chesapeake Bay. When exposed to high [DOC], Jug Bay soils adsorbed more than twice as much DOC as Taskinas soils under both saline and fresh conditions. Under fresh conditions, desorption occurred from both soils, but Taskinas released more DOC over time. These results are consistent with previous isotherm experiments (Pinsonneault et al., 2021 ) that revealed more net adsorption in the Jug Bay soils and more net desorption in the Taskinas soils. The mechanisms driving these differences could relate to soil biogeochemical properties, with Jug Bay soils having higher amounts of mineral oxides, higher surface areas, and lower concentrations of organic matter (Pondell & Canuel, 2022 ), and Taskinas having higher amounts of organic matter, lower abundance of mineral oxides, and lower specific surface area. These soil characteristics change the number of exchange sites, and combined with a concentration gradient of DOC, could determine the direction of DOC sorption (Kaiser & Zech, 1998 ; Kothawala & Moore, 2009 ; Kothawala et al., 2012 ). Chen et al. ( 2022 ) states that there could be a difference in sorption patterns over longer time scales than the 24 hours reported here, suggesting that amount of “sorption cycles” will shift what happens with adsorption as the soils go from more pristine to covered in OM. It is important to note, however, that the soil used in this experiment – taken from the upper 40 cm of the marsh sites – can be assumed to have already undergone many sorption cycles based on age, exposure, and the fact that a lot of organic matter is released from the soils themselves when introduced to the low [DOC] standard. The important role of soil mineral content, organic matter content, and [DOC] in regulating the magnitude and direction of sorption in coastal wetlands, as in many other ecosystems (Shields et al., 2016 ; Groeneveld et al., 2020 ; Pinsonneault et al., 2021 ), suggests that numerical models of DOC export can parameterize sorption processes from spatial databases of these characteristics similar to those used to model soil carbon stocks (Rovai et al., 2018 ; Twilley et al., 2018 ). Salinity, as expected, had a large role in sorption quantity. Pinsonneault et al. ( 2021 ) suggested that salinity most likely reduces DOC binding affinity and increases sorption capacity. Of these two effects, increased sorption capacity dominates even with the possible decreased binding affinity, as the presence of high salinity consistently reduced the net amount of desorption over time. When coupled with high [DOC], net adsorption of DOC more than doubled between fresh and saline treatments, with the extent of the interactions varying by site and [DOC]. Jug Bay soils were capable of adsorbing twice the amount of DOC in the saline treatment relative to the fresh treatment, and Taskinas soils switched from net desorption to net adsorption at higher salinity. The salinity of the sites where the soils were collected is likely to be important for explaining these differences in sorption kinetics. As a brackish marsh, Taskinas soils may be less responsive to shifts in salinity than Jug Bay, a tidal freshwater marsh. Our data suggest there will be changes in DOC sorption to soil surfaces, and therefore to DOC exchange with estuaries, as tidal freshwater marshes become more saline with sea level rise. Conversely, disturbances such as impoundments, increased river discharge, or increased precipitation intensity may cause tidal marshes to freshen (Portnoy, 1999 ; Kroeger et al., 2017 ), in which case soils similar to those at Taskinas could be susceptible to desorption under low salinity conditions (Reay & Moore, 2009 ). These are the first reported kinetic results from tidal marsh ecosystems, highlighting the importance of the influence of salinity and organic matter content on sorption processes, and thus potential retention of carbon in marsh soils. We found that high initial DOC concentration and high salinity primarily drove the observed reductions in desorption. The magnitudes differed between the two locations, but the patterns remained the same. This indicates the speed and direction of the net sorption process can be estimated based on initial conditions, while soil characteristics can modulate the magnitude of these processes. The influences of short- and long-term salinity changes on marsh systems have been studied in the last few decades due to concerns about the impacts of increased storm frequency and intensity, more severe droughts, and salt intrusion due to sea-level rise (SLR; Kirwan & Megonigal, 2013 ; Armitage et al., 2019 ; Charles et al., 2019 ; Spivak et al., 2019 ). Our kinetic incubations, with a maximum of 24-hour exposure of soils to higher salinity levels, represent the short-term effect of a change in salinity, but nonetheless provide vital information on how both freshwater and brackish marsh soils may respond to rapid changes in salinity and DOC concentration. This is directly relevant to storm-induced salinity changes, as the 24-hour timeline of the experiments captures the typical duration of storm surges (several hours to a couple days), and also shows how quickly these interactions would occur at the lowest elevations of the marsh by the creek edges (Danielson et al., 2018 ; Leonardi et al., 2018 ). There have been recent results suggesting the opposite effect of salinity on sorption, reporting less adsorption with higher salinity (Tomaszewski et al., 2021 ). We can most likely attribute these opposing results for this particular study, however, on several key differences in experiment design, such as: 1) lower initial DOC concentration (40 mg DOM L − 1 as compared with 280 mg DOC L − 1 ); 2) compositional differences in the soil compounds (specific Fe compound versus the complex marsh soil samples in this study); and 3) high levels of competition with other adsorbed ions that would likely limit adsorption extent in Tomaszewski et al. ( 2021 ). More importantly, Tomaszewski et al. ( 2021 ) found similar results in terms of speed and drivers - our sorption kinetic incubations reveal that sorption responses can be fast and responsive to different sets of initial conditions, and that both the speed and magnitude of change can vary strongly between soils with different geochemical characteristics. The studies also agree that preferential adsorption of high-molecular weight compounds occurs, and carbon compound fractionation happened over time to lead to different sorption patterns (both of which will be reported in a forthcoming manuscript). Although there were rapid DOC oscillations within minutes of the start of the incubation, the net DOC sorption processes were consistent and predictable under ideal conditions. Since the net DOC processes reached equilibrium within 24 hours and reacted similarly to the initial conditions across all incubations, it should be possible to model these processes as a function of just a few variables. The chemistry that drives the adsorption and desorption is complicated (Kleber et al., 2021 ), but salinity, [DOC] gradient between the soil and water, and the number and availability of adsorption sites (correlated with mineral concentrations) largely control DOC flux due to sorption. The addition of these processes to models such as the SFM (Di Toro, 2001 ) allows improved simulation of DOC fluxes from marshes with different soil characteristics and over a wide range of salinities (Morrissette, 2021 ). Kinetic models A suite of simplified zero-dimensional models was constructed to simulate the above experiments and calculate the adsorption and desorption rates. These models omitted several aspects of the SFM that were not relevant to the closed-environment laboratory experiments, such as oxygen, diffusion, biological activity, etc. Additionally, unlike the SFM, DOC in these models was not partitioned by lability levels which means that the derived rate parameters cannot necessarily be directly applied. Nonetheless, the zero-dimensional models provided important insights into the magnitude of these rate parameters and how they might change over time and space. As more information emerges on different classes of organic carbon through higher resolution techniques like ICP-MS, HRMS, etc, these models can be updated to provide more complex descriptions of dynamics. All versions of the models started with two ordinary differential equations that could be solved analytically (Herman, 2018). The solution to these equations with constant rate parameters produced a simple non-linear exponential decay or saturation response that could simulate adsorption and desorption over time, however, the model fitted with a single set of rate constants did not always capture the DOC mass in solution at steady state for both low and high initial conditions. Comparison of the different simplified model versions revealed which mathematical expressions of sorption best reproduced the experimental observations. When all the time points were included, the time-dependent model always gave the best fits to the data (results not shown; see Supplemental), revealing the necessity of changing the rate constants to capture rapid initial variation in DOC concentrations. Tuning the Linear and Langmuir models to the full data sets produced poor fits that gave too much weight to the first 3–10 minutes of the incubations (results not shown). Thus, in cases where very short-term variation (on the order of minutes) is important, these models may be inadequate when they are applied with constant rate parameters. On the other hand, when the models were fitted to the data without the initial oscillating time points, the performance of the Linear and Langmuir models improved, with better fits resulting from the Langmuir model, which showed that the equations with saturation are best suited for modeling DOC sorption processes over longer time scales. Most of the ΔDOC curves in Fig. 4 reveal an initial spike of net desorption followed by net adsorption. There are four possible explanations for this apparent reversal in sorption processes: 1) immediate dissolution of precipitated non-adsorbed freeze-dried DOC followed by adsorption of this DOC on the soils; 2) different rates of adsorption and desorption, with desorption occurring faster and therefore dominating the sorption processes early in the incubation, and adsorption occurring more slowly and therefore having greater influence later in the incubation; 3) competing influences of the initial conditions, with the lack of salt providing an environment more conducive to desorption, but the very high [DOC] ultimately overwhelming the system and forcing the DOC to adsorb onto the soils; and/or 4) preferential sorption of separate DOC fractions onto the soils, replacing and subsequently releasing previously sorbed DOC into solution. Assuming that there was DOC present in the pore water at time of core extraction, rewetting (scenario 1) could have added up to 2 mg L -1 (Jug Bay) or 4 mg L -1 (Taskinas) to the solution. This suggests that re-dissolution was < 10% of the initial peak (cf. Figure 4 ). So, this first possibility seems unlikely given our estimates of residual DOC in the sampled core, while the other three processes may have been occurring in tandem. Inferences about the fourth mechanism can be made by considering the contributions of two different DOC fractions, colored and non-colored DOC pools, to the sorption process at each time point. As reported by Morrissette ( 2021 ) and Neale et al. ( 2023 ) for the incubation conditions described here and in Pinsonneault et al. ( 2021 ), there is preferential adsorption of colored, non-native organic carbon from the solutions of Great Dismal Swamp DOC and preferential desorption of non-colored, native organic carbon from the soil pools. This indicates that an interesting, competing set of dynamics between adsorption and desorption of colored and non-colored DOC pools is occurring during the incubations. Sorption has also been shown to fractionate DOC incubated with other types of sorbants (desert soils, Fe minerals, marine sediment) in terms of its molecular composition, hydrophobicity, and isotopic composition (Avneri-Katz et al., 2017; Tomaszewski et al., 2021 ; Hauksson et al., 2023 ). Fractionation study results and their kinetics are forthcoming in Morrissette et al. ( in prep ), and not discussed here due to manuscript length. It should also be noted that the Langmuir model consistently and drastically improved the fits to the incubation data over the longer time scales when compared to the linear model. This indicates that the potential for saturation of adsorption was present, and that the experiments could not be accurately simulated without accounting for saturation. Even though DOC concentrations in the water overlying soils in these marshes are much lower -- recent values report an average DOC concentration of 5–6 mg L -1 across four years of sampling at Taskinas Marsh (Knobloch et al., 2021 ) and a maximum of 6 mg L -1 at Jug Bay (Logozzo et al., 2021 ) -- Pinsonneault et al. ( 2021 ) found evidence of potential saturation in the majority of the isotherm curves, especially under fresh conditions. It therefore seems most appropriate to use the saturated Langmuir equations with constant rate coefficients in the SFM. This is especially true given that SFM models are often applied at annual and multi-annual time scales. Implications Salinity and DOC concentration had large influences on the DOC sorption processes of two tidal marsh soils, with synergistic effects. It is important to understand these impacts because tidal marshes are experiencing changes in both characteristics, on tidal to decadal time scales. For example, with sea level rise introducing higher salinity to tidal marshes and higher inundation levels in marshes (Webb et al., 2013 ; Beckett et al., 2016 , Spivak et al., 2019 ), ionic perturbation of soils could increase bulk DOC sorption capacity, possibly preferentially releasing specific types of DOC compounds (e.g., noncolored; results forthcoming). In contrast, increased precipitation and freshwater runoff may decrease salinity in some cases. These competing influences could lead to rapid sorption interactions that influence organic carbon export to adjacent estuaries and oceans. This study was designed to isolate specific sorption processes on organic carbon concentrations while controlling for other factors that are relevant in situ. For example, incubations in a closed system do not allow for lateral export by horizontal flow and are therefore more representative of vertical DOC exchange by diffusion between marsh soils and soil porewater during an inundation event. But vertical diffusion is important to a marsh-wide DOC export budget because most influence of seawater comes from that tidal inundation over the soils (Guimond & Tamborski, 2021 ). In addition, groundwater export has been found to be much smaller than other pathways of exchange (Yelverton & Hackney, 1986 ; Czapla et al., 2020 ). Indeed, most studies emphasize the need to focus on processes at the soil surface-water column interface (French & Stoddart, 1992 ; Goni & Gardner, 2003). This study therefore focuses on what is, arguably, the dominant pathway of DOC export and thus captures the most relevant effects of changes in porewater DOC concentration and salinity on marsh-water DOC exchange. The models’ fits to the data provide a wide range of soil sorption responses under different initial conditions and salinity levels that produce a wide range of wetland sorption reaction rates. This leads to the question of how these rates should be used to inform SFMs. One thing was clear in our study: the sorption rates derived were 2 to 100 times faster than those that have been applied in previous SFM studies (Morrissette, 2021 ). Moreover, when these faster rates were used in the SFM they gave very different simulation results that may be more consistent with DOC fluxes that are observed in marsh systems (Morrissette, 2021 ). These results demonstrate that sorption processes should be included in all sediment flux models that track soil carbon flux to properly capture the fast reactions that can occur, especially if they are being used to simulate fluxes in response to perturbations. Inclusion of additional flux processes improves the confidence in abiotic transport of dissolved organic carbon and provides a better understanding of the temporal variability of tidal marsh DOC fluxes and budgets and their biogeochemical controls. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Funding This study was funded by National Science Foundation Grant DEB-1556556; NASA Grant NNX14AP06G; NSF-LTREB Program support of the Global Change Research Wetland (DEB-0950080, DEB-1457100, DEB-1557009); and the Smithsonian Environmental Research Center. This paper represents University of Maryland Center for Environmental Science Contribution No. 6300. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Hannah Morrissette, Patrick Neale, and Andrew Pinsonneault. The first draft of the manuscript was written by Hannah Morrissette and Patrick Neale, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgements Authors would like to thank the staff of the Jug Bay Wetlands Sanctuary, the York River State Park, the Virginia Institute of Marine Science (VIMS) Eastern Shore Laboratory, and the Great Dismal Swamp National Wildlife Refuge for their support and assistance. Specifically, we thank Michael Gonsior (University of Maryland Center for Environmental Science, Chesapeake Biological Laboratory), and Andrew Peresta (Smithsonian Environmental Research Center) for their assistance in the field and laboratory. Data Availability The datasets generated during and/or analyzed during the current study are available in the GitHub repository “Kinetic-Sorption-Incubation-Models”, with the model code and input files is available here: https://https://github.com/hannah-morrissette/Kinetic-Sorption-Incubation-Models . References Armitage AR, Weaver CA, Kominoski JS, Pennings SC (2019) Resistant to Hurricane Effects Varies Among Wetland Vegetation Types in the Marsh-Mangrove Ecotone. 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Estuar Coastal Shelf Sci 22(2):255–267. https://doi.org/10.1016/0272-7714(86)90116-2 Supplementary Files KineticSuppMorrissettesubmission12.27.23.pdf Cite Share Download PDF Status: Published Journal Publication published 18 Jul, 2024 Read the published version in Wetlands → Version 1 posted Reviewers agreed at journal 19 Jan, 2024 Reviewers invited by journal 15 Jan, 2024 Editor invited by journal 08 Jan, 2024 Editor assigned by journal 28 Dec, 2023 First submitted to journal 27 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Morrissette","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYLCCBAjFxvCBQQLBJaiFh42BjXEG0VoYoFqYeZAtxQV023uffXjwp07OXr752GObPxYM/Ow5Bni1mJ05bjwjse2wMQ8bW7pxbpsEg2TPGwJabqQxMyQ2HEjsYeMxk85tkGAwuEHIFpCWhD919T1s/N+kLf5IMNgTp4WNOYGHjYdNmoENaIsEQb8cAzqs7bBhz7E0M8neNgkeiTPPCvBrOd7GzPjjT508e/PhZxJAhhx/e/IGvFowAA9pykfBKBgFo2AUYAUAS2084iY0aQEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-0511-3659","institution":"Smithsonian Environmental Research Center","correspondingAuthor":true,"prefix":"","firstName":"Hannah","middleName":"K.","lastName":"Morrissette","suffix":""},{"id":267373602,"identity":"7ea93693-ee31-499f-a705-bc62b9a01a1e","order_by":1,"name":"Patrick J. Neale","email":"","orcid":"","institution":"Smithsonian Environmental Research Center","correspondingAuthor":false,"prefix":"","firstName":"Patrick","middleName":"J.","lastName":"Neale","suffix":""},{"id":267373603,"identity":"a439138d-102d-445a-8457-798b0428c8be","order_by":2,"name":"J. Patrick Megonigal","email":"","orcid":"","institution":"Smithsonian Environmental Research Center","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"Patrick","lastName":"Megonigal","suffix":""},{"id":267373604,"identity":"1295bde2-52b2-44fd-a0db-3004bd5a5c73","order_by":3,"name":"Maria Tzortziou","email":"","orcid":"","institution":"City College of New York: The City College of New York","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Tzortziou","suffix":""},{"id":267373605,"identity":"1981b67a-83f1-41c7-ba61-228f5b5dbaeb","order_by":4,"name":"Elizabeth A. Canuel","email":"","orcid":"","institution":"VIMS: William \u0026 Mary Virginia Institute of Marine Science","correspondingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"A.","lastName":"Canuel","suffix":""},{"id":267373606,"identity":"ae1ef38b-f772-4cbf-a743-9538c2c4b0fe","order_by":5,"name":"Andrew J. Pinsonneault","email":"","orcid":"","institution":"Smithsonian Environmental Research Center","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"J.","lastName":"Pinsonneault","suffix":""},{"id":267373607,"identity":"81392d32-1bb2-4386-81c4-762a0c43a06b","order_by":6,"name":"Raleigh R. Hood","email":"","orcid":"","institution":"Horn Point Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Raleigh","middleName":"R.","lastName":"Hood","suffix":""}],"badges":[],"createdAt":"2023-12-27 18:48:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3813404/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3813404/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13157-024-01835-2","type":"published","date":"2024-07-19T00:27:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49824514,"identity":"23f2173b-ed19-420e-bb82-020b9a135627","added_by":"auto","created_at":"2024-01-18 15:37:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":449463,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the four study sites and the location of the Great Dismal Swamp. (From Pinsonneault et al., 2021).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-3813404/v1/0880a1e531cc2a6558d246f6.png"},{"id":49823008,"identity":"7f89fbf6-9974-434c-bb9e-05de6f806bdb","added_by":"auto","created_at":"2024-01-18 15:29:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62978,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea-b. \u003c/strong\u003eConceptual diagrams of the closed incubation experiments tracking the bulk organic carbon pools (a) pre- and (b) post-exchange. Once DOC has adsorbed to the soil, or Cs has desorbed to solution, they are then modeled as part of the new pool.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-3813404/v1/ec2da9cf6c35b95bbf8875b2.png"},{"id":49823010,"identity":"8c0e1f72-4730-44d3-91d1-dcf556476257","added_by":"auto","created_at":"2024-01-18 15:29:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":976718,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea-b. \u003c/strong\u003eΔ[DOC] (final-initial) over time for (a) Jug Bay and (b) Taskinas sites. Negative values indicate net adsorption for that time point, whereas positive values indicate net desorption. The four colors represent the four stock solutions as described in the methods section 2.3: HF (blue solid line); HS (red dashed line); LF (yellow solid line); LS (purple dashed line). Data are for the full incubation time of 24 hours.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-3813404/v1/6dce7f68e7af0d3435ae9cd3.png"},{"id":49823011,"identity":"e774213e-f690-44f0-98bd-4b4fdbd760ac","added_by":"auto","created_at":"2024-01-18 15:29:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":318162,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea-b. \u003c/strong\u003eKinetic Δ[DOC] (final-initial) over time. Negative indicates net adsorption for that time point, while positive values indicate net desorption. The four colors represent the four stock solutions as described in the methods section 2.3. Data are shown for the first 30 minutes.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-3813404/v1/17af45fedeaac61eb1923179.png"},{"id":49824513,"identity":"898ff941-0378-466e-80fe-5ff1c1c43581","added_by":"auto","created_at":"2024-01-18 15:37:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":145147,"visible":true,"origin":"","legend":"\u003cp\u003eNon-linear regression fits of the analytical solution for the linear model (dashed line) to the observed (points) Jug Bay DOC mass in solution over time: a) JBHF (pink), JBLF (red); b) JBHS (pink), JBLS (red). RMSE values are a combined performance of both high and low [DOC] for each salinity treatment.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-3813404/v1/2e8d4f6723d9e99fb4411cf4.png"},{"id":49825756,"identity":"b55a24eb-2ef8-4c62-9cbe-d406e783c2eb","added_by":"auto","created_at":"2024-01-18 15:45:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":152993,"visible":true,"origin":"","legend":"\u003cp\u003eNon-linear regression fits of the numerical solution to the \u003cem\u003eLangmuir \u003c/em\u003emodel (green solid line) to the observed (solid points) Jug Bay DOC mass in solution over time: a) JBHF (light green), JBLF (dark green); b) JBHS (light green), JBLS (dark green). RMSE values are a combined performance of both high and low [DOC] for each salinity treatment.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-3813404/v1/2310292e9422953c172aee20.png"},{"id":49824515,"identity":"6c053cc0-9438-47ba-add9-ef0a24101331","added_by":"auto","created_at":"2024-01-18 15:37:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":184301,"visible":true,"origin":"","legend":"\u003cp\u003eIsotherm curves plotted as the change in DOC at time zero - at equilibrium (Δ[DOC]\u003csub\u003e0-f\u003c/sub\u003e (mg-DOC g soil\u003csup\u003e-1\u003c/sup\u003e)) versus the equilibrium DOC ([DOC]\u003csub\u003ef\u003c/sub\u003e (mg-DOC g soil\u003csup\u003e-1\u003c/sup\u003e)). Each color and shape represent initial salinity conditions (Jug Bay 0 psu = pink circle (JBF), Jug Bay 35 psu = green square (JBS), Taskinas 0 psu = blue diamond (TAF), Taskinas 35 psu = purple triangle (TAS)). The shaded area shows the 95% confidence interval for the isotherm prediction based on error propagation from the fit of Pinsonneault et al. (2021). Only the fitted isotherm is shown for TAS since the confidence interval could not be estimated (more details in text). All seven of the [DOC] treatments from Pinsonneault et al. (2021) are plotted with the 24hr kinetic data points added (circled).\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-3813404/v1/80534c1aae729bdbfabab974.png"},{"id":60717278,"identity":"6802c47b-05a9-4362-9fa7-8971f2b1ee0b","added_by":"auto","created_at":"2024-07-20 00:27:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3293283,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3813404/v1/18626268-5af4-4a4b-88b0-0009ad5ddfbd.pdf"},{"id":49823015,"identity":"82645af7-b6fe-4507-a3a5-c87c907a754c","added_by":"auto","created_at":"2024-01-18 15:29:21","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":4688454,"visible":true,"origin":"","legend":"","description":"","filename":"KineticSuppMorrissettesubmission12.27.23.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3813404/v1/967a8f92bd5b692245288616.pdf"}],"financialInterests":"","formattedTitle":"Wetland soil characteristics influence the kinetics of dissolved organic carbon sorption","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWetlands are among the most productive ecosystems on earth, and they exert a strong influence on dissolved organic carbon (DOC) export to aquatic ecosystems. Tidal marshes are important sources of DOC to estuaries and coastal waters (Tzortziou et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Herrmann et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Najjar et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), resulting in as much as 80% of the annual lateral DOC and dissolved inorganic carbon (DIC) export to oceans (Wang \u0026amp; Cai, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Despite occupying less than 5 percent of the total land surface, tidal wetlands disproportionately contribute to coastal productivity, carbon fluxes, and soil carbon accumulation (Pendleton et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Najjar et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Windham-Myers et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eQuantifying the coastal carbon flux is challenging due to the extensive spatial and temporal heterogeneity across interacting coastal ecosystems, but necessary due to the importance of coastal ecosystems in the global carbon cycle. Models such as the Sediment Flux Model (SFM; Di Toro, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Brady et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Testa et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) attempt to accurately simulate observed nutrient and carbon exchange and transformation within coastal soils and dissolved organic carbon was recently added to the SFM (Clark et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This was done to address the gap between the increasing number of studies that show that DOC is a large and important component of the coastal carbon budget, and the fact that DOC is rarely addressed in sediment-water column flux models (Burdige et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Clark et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSorption is an important process to quantify in sediment-water column flux models because it regulates the partitioning of DOC between water and soil surfaces. Many studies in a variety of ecosystems have demonstrated that soils and sediment can readily adsorb DOC, particularly those dominated by poorly crystalline iron and aluminum oxides with high surface area and sorption capacity (Kothawala et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Shaker et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pinsonneault et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Isotherms are typically used to quantify the sorption characteristics of soils and sediments. This approach involves adding DOC of a known initial concentration to soils and allowing time for simultaneous sorption and desorption processes to reach thermodynamic equilibrium (Qualls \u0026amp; Richardson, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Equilibrium isotherms have proven valuable for understanding the amount of DOC that soils adsorb or desorb as a function of surface properties, environmental conditions, and other physicochemical factors (Vandenbruwane et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, they do not quantify rates of DOC sorption and desorption that are critical for parameterizing sediment-water column flux models.\u003c/p\u003e \u003cp\u003eLaboratory incubations and in situ measurements suggest that adsorption and desorption are rapid processes (Kaiser \u0026amp; Zech, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Kothawala et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Shaker et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In 24-hour batch sorption experiments where DOC-free solution was added to forest soils, the authors reported up to 75% of the sorption processes happening in the first 15 minutes, and 90% occurring by the end of 4 hours (Kaiser \u0026amp; Zech, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Another study, where sorption onto mineral soils was tested under varying levels of pH and other conditions, found that equilibrium occurred in less than 30 minutes regardless of initial conditions (Shaker et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Other kinetic studies indicate that adsorption and desorption are time-dependent and competitive, with adsorption rates slowing over time and sometimes inducing desorption depending on the compounds (Lilienfein et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Koopal et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To our knowledge, however, there has been only one equilibrium isotherm study with DOC from coastal wetlands soils and soils (Pinsonneault et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and no studies of DOC sorption-desorption kinetics.\u003c/p\u003e \u003cp\u003eIncorporating coastal wetland soils into DOC transport experiments is vital due to the influence of salinity on its exchange. Salinity has been shown to have an impact on DOC equilibrium, as changes in ionic strength lead to competition at exchange sites on the soil surface (Seitzinger et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Stumm \u0026amp; Morgan, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Tidal marshes range in salinity from freshwater to fully saline sea water and are susceptible to changes in salinity driven by precipitation, sea level rise, storm flooding, and human activity. It has also been shown that salinity impacts sorption capacity and binding affinity (Pinsonneault et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which inherently affects the speed at which sorption processes can occur. We hypothesized that DOC export from tidal marshes varies with soil type and salinity, and that it was possible to capture this variability in DOM transport models.\u003c/p\u003e \u003cp\u003eWhile previous isotherm experiments on coastal wetland soils were instructive as to how soil composition affected net DOC exchange (Pinsonneault et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), they did not answer three questions that are key to modeling: 1) how fast are the sorption processes? 2) do the sorption rates vary as a function of time? and 3) how much time is needed to reach steady state? We addressed the lack of DOC sorption-desorption kinetics data in aquatic ecosystems generally and the absence of such data for tidal wetlands specifically by quantifying rates for multiple marsh soil types and initial conditions in closed laboratory soil incubations. We then formulated a suite of simplified numerical sorption models to simulate the zero-dimensional conditions in these kinetic sorption experiments, providing insights into the dynamics of the sorption processes over time and rate parameters that are needed to include sorption processes in more complex sediment flux models like the SFM.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eKinetic isotherm experiments\u003c/h2\u003e\n \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\n \u003ch2\u003eStudy sites\u003c/h2\u003e\n \u003cp\u003eSoils were collected from two of the four tidal marshes in the Chesapeake Bay estuary along the Northeast US that were the subject of isotherm studies in Pinsonneault et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e): (1) Kirkpatrick Marsh in Edgewater, Maryland, U.S.A. (36\u0026deg;53\u0026rsquo;N, 76\u0026deg;33\u0026rsquo;W), (2) Jug Bay Wetlands Sanctuary in Lothian, Maryland, U.S.A. (38\u0026deg;46\u0026rsquo;N, 76\u0026deg;42\u0026rsquo;W), (3) Taskinas Marsh near Williamsburg, Virginia, U.S.A. (37\u0026deg;25\u0026rsquo;N, 76\u0026deg;43\u0026rsquo;W), and (4) Wachapreague Marsh in Wachapreague, Virginia, U.S.A. (37\u0026deg;32\u0026rsquo;N, 75\u0026deg;41\u0026rsquo;W) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). These four systems capture a wide range of marsh characteristics such as salinity, soil organic matter (SOM), and plant biodiversity (Pondell \u0026amp; Canuel, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The variability encompassed by these sites provided insights into the soil characteristics that are critical to formulate and parameterize numerical models. A detailed description of all four sites can be found in Pinsonneault et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Pondell \u0026amp; Canuel (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). For this study, we chose two sites that bracket the full range of soil exchange characteristics of the four sites as reported by Pinsonneault et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e): Jug Bay, which had the highest equilibrium net-adsorption at high salinity, and Taskinas, which had the highest net-desorption under freshwater conditions (0 salinity).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eExperimental design\u003c/h2\u003e\n \u003cp\u003eSoils were subjected to two end-member salinity treatments (0 psu, 35 psu) and two end-member DOC treatments (0 mg L\u003csup\u003e-1\u003c/sup\u003e, 275 mg L\u003csup\u003e-1\u003c/sup\u003e). Rates were measured at seven time points during those 24 hours, with the first four time points taken at short intervals over the first hour to capture potentially rapid sorption processes (Kaiser \u0026amp; Zech, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e; Shaker et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eStock creation\u003c/h2\u003e\n \u003cp\u003eFollowing the protocol set forth in Pinsonneault et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), surface water was collected in August 2018 from the Jericho Ditch of the Great Dismal Swamp Wildlife Refuge, VA (36\u0026deg;41\u0026rsquo;45.03\u0026rdquo;N, 76\u0026deg;30\u0026rsquo;28.16\u0026rdquo;W) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) to create a concentrated DOC stock inoculant. 120 L of highly colored water was concentrated via reverse osmosis, filtered through 0.2 \u0026micro;m to remove particulates, and stabilized with sodium azide (NaN\u003csub\u003e3\u003c/sub\u003e; 1 \u0026micro;M) to eliminate microbial activity. This stock solution was the fresh high-DOC initial concentration. Instant Ocean was added to a sub-volume to reach 35 psu salinity and pH, salinity, and conductivity were measured for both solutions. A parallel low-DOC stock was prepared by adding an equivalent quantity of NaN\u003csub\u003e3\u003c/sub\u003e to the equivalent volume of DI water. Hydrochloric acid (HCl) was added until the pH matched the concentrated stock solution, then Instant Ocean was added to a subset of the dilutant until the salinity and conductivity matched the high-salinity concentrated DOC stock solution. This procedure produced four stock solutions: high DOC\u0026thinsp;+\u0026thinsp;no salt (HF), high DOC\u0026thinsp;+\u0026thinsp;salt (HS), low DOC\u0026thinsp;+\u0026thinsp;no salt (LF), and low DOC\u0026thinsp;+\u0026thinsp;salt (LS). Optical properties and total organic carbon concentration were measured for all final stock solutions, as well as throughout the preparation process to ascertain the effects of each preparation step on the DOC quantity and quality. Absorbance did not differ significantly throughout the process (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.989). Slopes of CDOM absorption spectra over the wavelength ranges of 275 to 295 nm (S\u003csub\u003e275-295\u003c/sub\u003e) and 350 to 400 nm (S\u003csub\u003e350-400\u003c/sub\u003e), among other indicators of DOC composition, only varied by a standard deviation of \u0026plusmn;\u0026thinsp;1.3% (S\u003csub\u003e275-295\u003c/sub\u003e) and \u0026plusmn;\u0026thinsp;1.8% (S\u003csub\u003e350-400\u003c/sub\u003e) across all five iterations of retentate. From this, it was concluded that the compounds within the stock solution remained stable in source and chemical make-up. Concentrations did not differ significantly (p\u0026thinsp;=\u0026thinsp;0.187) between steps except for post-reverse osmosis when the DOC concentration was purposefully increased by three-fold.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eExperimental procedure\u003c/h2\u003e\n \u003cp\u003eThe experimental procedure and pre- and post-analyses followed Pinsonneault et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), using the same cored, dried, and homogenized soils from the isotherm experiments and stored properly in a desiccator between incubations. The aqueous phase of each soil slurry was measured for total organic carbon concentration and spectral properties. The soil slurry itself had a volume to mass ration of 30 mL of solution to 1 g of soil. These included Excitation-Emission Matrices (EEMs), Humification Index (HIX), Freshness Index (\u0026beta;:\u0026alpha;), Biological Index (BIX), and Fluorescence Index (FI) on a FluoroMax 3 spectrofluorometer for information on the composition of the DOC compounds (as per Helms et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Hansen et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Absorbance was measured on a Thermoscientific spectrophotometer, and total organic carbon (TOC) was measured on a Shimadzu TOC-L for the quantity of DOC throughout the incubations. Optical properties were measured to determine the quality of the DOC and how it shifted through time due to sorption, providing information on molecular weight, source, and partitioning between colored and non-colored fractions. The detailed methods and results of these measurements can be found in (Morrissette, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pinsonneault et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eKinetic models\u003c/h2\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003eParameterization\u003c/h2\u003e\n \u003cp\u003eThe subsequent sections describe the formulation and parameterization of a suite of carbon-based zero-dimensional models that were developed to simulate the laboratory kinetic sorption incubations detailed above. The model formulations assumed that the organic carbon exchanges in the laboratory incubations were driven by adsorption and desorption processes. However, it should be noted that these incubations used material from field-moist cores that were freeze-dried before preparation of soil fractions. Thus, any DOC that was present in the water fraction of the core may have contributed to DOC exchange upon re-wetting of the soil due to the re-dissolution of this residual DOC fraction. This potential contribution was conservatively estimated by assuming any water present in the core at the time of extraction contained DOC at the pore water concentration, and all of that DOC was re-dissolved into the incubation solution. Water content was obtained by oven drying the cores and DOC was measured in pore water sampled from soil \u0026quot;peepers\u0026quot; (A. J. Pinsonneault \u003cem\u003eunpubl\u003c/em\u003e.).\u003c/p\u003e\n \u003cp\u003eOptical analyses described in Morrissette (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Neale et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e) showed that multiple pools of organic carbon were involved in the exchange. However, only the bulk DOC pool, soil-associated organic carbon (Cs), and solution-associated DOC were modeled in this study. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows a simplified conceptual model where at time 0 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea), Cs and DOC were separated, followed by exchange over time with sorption of the bulk DOC (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). Once DOC was adsorbed to the soil, it was considered a part of the Cs pool, and vice versa.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThe experiments were modeled mathematically using two alternative sets of differential equations that simulated the rate of DOC movement in the closed experiments due to adsorption and desorption. The simplest were \u003cem\u003eLinear\u003c/em\u003e equations (Eqs.\u0026nbsp;1 \u0026amp; 2) solved analytically, whereas the more complex were \u003cem\u003eLangmuir\u003c/em\u003e equations (Eqs.\u0026nbsp;5 \u0026amp; 6) solved numerically. In both cases, equation parameters that provided the best fit to the experimental results were determined by least-squares minimization (non-linear regression). A third \u003cem\u003eTime-dependent\u003c/em\u003e model was formulated and coded to explicitly capture the rapid, initial oscillations in DOC sorption by allowing the desorption parameter to change over time. It is only briefly discussed here. See Morrissette (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and the supplementary material for the full equations, parameter descriptions, and results.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003eLinear model\u003c/h2\u003e\n \u003cp\u003eEquations\u003c/p\u003e\n \u003cp\u003eAssuming first order ordinary differential equations (ODEs) for the exchange described above (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), a mathematical representation of the sorption experiments was formulated as follows:\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003cp\u003e(1) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{dCs}{dt}\\)\u003c/span\u003e\u003c/span\u003e= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(-{k}_{des}\\times Cs + {k}_{ads}\\times DOC\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003cp\u003e(2) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{dDOC}{dt}\\)\u003c/span\u003e\u003c/span\u003e= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{des}\\times Cs - {k}_{ads}\\times DOC\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003ewhere each equation represents the change in organic carbon mass over time in either the solution (DOC) or soil (Cs). Both pools have an associated rate parameter; desorption (k\u003csub\u003edes\u003c/sub\u003e) associated with the soil pool, and adsorption (k\u003csub\u003eads\u003c/sub\u003e) associated with the solution pool. The solution pool loses mass due to adsorption (leaving solution to attach to soils) and gains mass from desorption (loss from soils to solution), whereas for the soil pool the opposite is true. This is termed the \u003cem\u003eLinear\u003c/em\u003e model since exchanges are a linear function of the mass in each pool. Equations were mass-specific since the mass of soil (1 g) and volume of solution (30 mL) were constant throughout the experiments. Parameter descriptions and units can be found in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eAnalytical solution and fitting\u003c/p\u003e\n \u003cp\u003eEquations\u0026nbsp;1 \u0026amp; 2 are linear first order ODEs with constant coefficients (k\u003csub\u003edes\u003c/sub\u003e; k\u003csub\u003eads\u003c/sub\u003e). Following a well-known process for solving a homogeneous system through substitution of the unknowns and finding the roots of the equation (Herman, 2018), a generic solution for each equation was calculated, solved, and simplified to be:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$Cs\\left(t\\right)= \\frac{1}{{k}_{ads}+{k}_{des}}\\left[Cs\\left(0\\right)\\left({k}_{ads}+{k}_{des}{e}^{-\\left({k}_{ads}+{k}_{des}\\right)t}\\right) + DOC\\left(0\\right){k}_{ads}\\left(1- {e}^{-\\left({k}_{ads}+{k}_{des}\\right)t}\\right)\\right]$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equb\" class=\"mathdisplay\"\u003e$$DOC\\left(t\\right)= \\frac{1}{{k}_{ads}+{k}_{des}}[Cs\\left(0\\right)kdes\\left(1- {e}^{-\\left({k}_{ads}+{k}_{des}\\right)t}\\right)+DOC\\left(0\\right)\\left({k}_{des}+{k}_{ads} {e}^{-\\left({k}_{ads}+{k}_{des}\\right)t}\\right)]$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003cp\u003eUsing the \u0026ldquo;fitnlm\u0026rdquo; function in Matlab 2020a, time (t) was provided along with values for DOC(0) and Cs(0). Cs at time 0 was determined from the native desorbable carbon (C\u003csub\u003e0\u003c/sub\u003e) pool calculations, described in Pinsonneault et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Initial estimates for k\u003csub\u003eads\u003c/sub\u003e and k\u003csub\u003edes\u003c/sub\u003e were passed to the function, which used the input parameters to fit a curve to the observational DOC data and estimate best parameter values for k\u003csub\u003eads\u003c/sub\u003e and k\u003csub\u003edes\u003c/sub\u003e as the relative unknowns. Initial data points that created a sorption \u0026ldquo;spike\u0026rdquo; were omitted during the fitting process, as the \u003cem\u003eLinear\u003c/em\u003e model could not capture the directional shift. When analyzing the sorption isotherms from Pinsonneault et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), it is apparent that regardless of the initial concentration of DOC in solution, the range of isotherm values per salinity level could be fit with one curve from one isotherm equation. This indicates that, for this kinetic model, one set of k\u003csub\u003eads\u003c/sub\u003e and k\u003csub\u003edes\u003c/sub\u003e parameters should be able to be applied to both the high [DOC] and low [DOC] kinetic results within one site. However, k\u003csub\u003eads\u003c/sub\u003e and k\u003csub\u003edes\u003c/sub\u003e should differ between the fresh and saline initial conditions since the isotherms vary with salinity level. Thus, for each site we obtained one set of best fit coefficients for each set of combined data from incubations with high [DOC] and low [DOC] initial conditions.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eLangmuir model\u003c/h2\u003e\n \u003cp\u003eBased on the isotherm curves from Pinsonneault et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), saturation of adsorption could be expected for sufficiently high DOC, especially at lower salinity. Since the stock inoculant for the kinetics incubations reported here had an even higher concentration of DOC than used by Pinsonneault et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), adsorption saturation on the Taskinas and Jug Bay soils theoretically could have occurred. A version of the model was therefore formulated that included a saturation coefficient that allowed sorption to slow or stop when approaching the limit of available adsorption sites over time. Parameter descriptions can be found in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eEquations\u003c/p\u003e\n \u003cp\u003eSaturation was introduced into equations 1 \u0026amp; 2 as follows:\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003cp\u003e(5) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{dCs}{dt}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(-{k}_{des}\\times Cs +{{k}_{ads}}_{2}\\times DOC\\times S\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003cp\u003e(6) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{dDOC}{dt}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({k}_{des}\\times Cs-{{k}_{ads}}_{2}\\times DOC\\times S\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003ewhere k\u003csub\u003eads2\u003c/sub\u003e is a second-order rate constant with units of mg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and the adsorption terms were multiplied by the saturation coefficient (S; mg). S was calculated as:\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003cp\u003e(7)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(S = Qmax - Cs\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003ewhere Q\u003csub\u003emax\u003c/sub\u003e (mg) is the maximum adsorption capacity for the soil and Cs (mg) is the mass on the soil. Adding the saturation coefficient, assuming a monolayer adsorption scheme, controls the rate of adsorption based on the adsorption capacity of the soils. If Q\u003csub\u003emax\u003c/sub\u003e increases or Cs decreases, the rate of adsorption could increase due to a larger S, and if the opposite occurs, the rate of adsorption could slow or stop. Solving Eq.\u0026nbsp;6 for equilibrium DOC (DOC\u0026apos;=0) given initial values of soil OC (C\u003csub\u003e0\u003c/sub\u003e) and DOC yields the Langmuir isotherm (Eq.\u0026nbsp;8, see below, K\u0026thinsp;=\u0026thinsp;k\u003csub\u003eads2\u003c/sub\u003e/k\u003csub\u003edes\u003c/sub\u003e). As with the linear model, initial rapid exchanges of DOC that often occurred in the experiments could not be simulated by this Langmuir model, so these initial data points were omitted during fitting. Also, volume (of solution) and mass (of soil) are not included in the equations as they were constant throughout the experiments. If being used in generalized models, the equations and values would have the units of volume and mass added back in for unit uniformity, e.g. k\u003csub\u003eads2\u003c/sub\u003e would have units of L mg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (cf. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Equations in generalized format can be found in the supplementary file (Table A.4).\u003c/p\u003e\n \u003cp\u003eInitial values\u003c/p\u003e\n \u003cp\u003eSince the soil samples used in the incubations were the same as Pinsonneault et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), fixed parameter values for Q\u003csub\u003emax\u003c/sub\u003e and Cs at time 0 were used from their fitted Langmuir isotherms. DOC at time 0 was always set to be the pre-incubation observed DOC value from laboratory analysis. Initial values for all the simulations can be found in supplementary (Appendix A). The fitting scheme, using \u0026ldquo;ode23\u0026rdquo; in Matlab 2020a, performed best-fit analysis as in the linear model, simultaneously analyzing high and low [DOC] data sets for one set of rate coefficients.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eModel parameters, descriptions, and units. Values of each parameter for time\u003csub\u003e0\u003c/sub\u003e were scenario-dependent (Appendix A).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel Version\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ek\u003csub\u003edes\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDesorption rate coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear, Langmuir\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ek\u003csub\u003eads\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdsorption rate coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCs(t)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMass of organic carbon adsorbed on soils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDOC(t)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMass of DOC free in solution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSaturation coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLangmuir\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum adsorption capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLangmuir\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ek\u003csub\u003eads2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSaturation adsorption rate coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLangmuir\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eAnalysis\u003c/h2\u003e\n \u003cp\u003eTwo-sample t-tests (assuming equal variances) were used to test the difference between sites for each initial condition and the percent of sorption completed over time and to compare the effects of salinity and DOC concentration within one site. Confidence intervals for the original isotherm curves were calculated via error propagation of the statistical uncertainty in the fitted parameters of the (non-linear) Langmuir isotherm equation:\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003cp\u003e(8) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varDelta \\left[DOC\\right]}_{0-f} = {\\frac{(Qmax \\times K \\times [DOC\\left]f\\right)}{(1 + (K \\times \\left[DOC\\right]f\\left)\\right)}}_{}\\)\u003c/span\u003e\u003c/span\u003e- \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(C0\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003ewhere [DOC]\u003csub\u003e0-f\u003c/sub\u003e is the change in DOC concentration subtracting final values from initial, Q\u003csub\u003emax\u003c/sub\u003e is the maximum sorption capacity, K is the binding affinity, [DOC]\u003csub\u003ef\u003c/sub\u003e is the final DOC concentration in solution, and C\u003csub\u003e0\u003c/sub\u003e is the amount of desorbable organic carbon on the wetland soils.\u003c/p\u003e\n \u003cp\u003eFour sets of data from the Langmuir isotherm experiments (Pinsonneault et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) were chosen to be directly compared to the kinetic data: Jug Bay Fresh (JBF, 0 psu), Jug Bay Saline (JBS, 35 psu), Taskinas Fresh (TAF, 0 psu), and Taskinas Saline (TAS, 35 psu). The full range of [DOC] values for each of the four were plotted, then the matching 8 points chosen from relevant kinetic data \u0026ndash; the low and high DOC concentrations of JBF, JBS, TAF, and TAS \u0026ndash; were graphed onto those curves. For the high [DOC] data points, the kinetic points were checked against the original Langmuir isotherm curve confidence intervals. For TAS, the confidence interval was not included. Since it was more linear than the others and did not indicate any approach in saturation, the non-linear fit was not able to obtain an acceptable value of Q\u003csub\u003emax\u003c/sub\u003e. Instead, the kinetic point was compared to an isotherm that was estimated using an assumed Q\u003csub\u003emax\u003c/sub\u003e.\u003c/p\u003e\n \u003cp\u003eThe best-fit parameters for the linear and Langmuir model were estimated in Matlab 2020a using the \u0026ldquo;nlmfit\u0026rdquo; function. The Langmuir numerical model was coded and analyzed in Matlab 2020a using the \u0026ldquo;ode23\u0026rdquo; function. Additional packages within RStudio of \u0026ldquo;dplyr\u0026rdquo;, \u0026ldquo;tidyr\u0026rdquo;, \u0026ldquo;patchwork\u0026rdquo;, and \u0026ldquo;ggplot2\u0026rdquo; were used for data manipulation and graphing. A GitHub repository \u0026ldquo;Kinetic-Sorption-Incubation-Models\u0026rdquo; with the model code and input files is available here: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://https://github.com/hannah-morrissette/Kinetic-Sorption-Incubation-Models\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eRoot mean square error (RMSE), model efficiency (MEF; Stow et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e), average absolute error (AAE), adjusted R\u003csup\u003e2\u003c/sup\u003e, and Spearman rank correlation values were calculated to assess model performance. RMSE is shown on the graphs in the \u003cspan class=\"InternalRef\"\u003eresults\u003c/span\u003e section and ranked tables can be found in the supplementary information (Table B.1). As described previously, \u0026ldquo;peaks\u0026rdquo; in the data were omitted for the linear and Langmuir models before calculating these model performance metrics.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eKinetic incubations\u003c/h2\u003e \u003cp\u003eThe two tidal wetland soils subjected to four sets of initial conditions (HF, HS, LF, LS) generated both net adsorption (negative Δ[DOC] (y-axis)) and net desorption (positive Δ[DOC] (y-axis)) patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For Jug Bay soils (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), 77.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1% of the net exchange occurred within 15 minutes. Net adsorption occurred when initial [DOC] was high (HF and HS incubations), while net desorption occurred when initial [DOC] was low (LF and LS treatments), with no significant difference between LF and LS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.967, two-sample t-test). In contrast, there was a doubling of the amount of adsorption that occurred between the HF and HS treatments, revealing that the effects of salinity on sorption were amplified under the high [DOC] conditions. The four incubations reached equilibrium at a Δ[DOC] of -53.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (HF), -124.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (HS), 34.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (LF), and 34.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (LS).\u003c/p\u003e \u003cp\u003eTaskinas soils followed similar patterns in sorption processes with time (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), with an average of 74.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1% of sorption completed within 15 minutes, reaching relative equilibrium before 6 hours. However, in contrast to the Jug Bay soil, there was net desorption in the HF treatment such that the only treatment with net adsorption was HS. The magnitude of adsorption in the HS treatment was much lower than in the HF treatment, but the absolute difference between the two was similar to the difference in the same treatments for Jug Bay. LF and LS were significantly different for Taskinas (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.76 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e, two-sample t-test), indicating a stronger response to the initial conditions. Equilibrium values for the change in [DOC] were 25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (HF), -47.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (HS), 50.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (LF), and 33.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (LS).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn average, 76.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1% and 83.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1% of the total sorption, pooled across both Jug Bay and Taskinas soils, was completed in the first 15 minutes and 1 hour, respectively, with 93.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0% of sorption at 6 hours (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results confirm that 24 hours was sufficient time for sorption isotherm incubations of these wetland soils. Taskinas processes took slightly more time to reach equilibrium than Jug Bay based on percent completion of total exchange for each time point, but the difference was not statistically significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eKinetic model solutions\u003c/h2\u003e \u003cp\u003eFor brevity, the \u003cem\u003eLinear\u003c/em\u003e and \u003cem\u003eLangmuir\u003c/em\u003e solutions are reported and discussed for only the four sets of initial conditions (HF, HS, LF, and LS) for the Jug Bay experiments described above. These results can be taken as representative of all the model fits to the experiments described in this manuscript (see supplemental appendices C-F) and samples from other sites (Morrissette, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003eLinear solution\u003c/h2\u003e \u003cp\u003eThe fits to Jug Bay kinetic data followed either exponential decay or saturation functions depending on the parameter values. When DOC was initially high the experiment was dominated by adsorption over time and the non-linear regression fits to the analytical solution of the linear model captured this decay, while the opposite was true when DOC was initially low. In most cases the regressions that used a common set of k\u003csub\u003eads\u003c/sub\u003e and k\u003csub\u003edes\u003c/sub\u003e for high and low DOC provided poor fits to the observed data, signifying that the simplest linear equations alone cannot capture the sorption incubation results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). High salinity affected the regression fit the most, as the converged set of parameters still resulted in substantial overestimation of DOC over time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eLangmuir solution\u003c/h2\u003e \u003cp\u003eIntroducing a saturation coefficient to the ODEs improved the model fits across all cases, especially for the high salinity incubations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Occasionally, the inflection points of the curves sharpened slightly compared to the \u003cem\u003eLinear\u003c/em\u003e model. The fits for the Taskinas data (Appendix D) depicted a similar pattern of a major improvement over the fits to the linear model, suggesting that saturation is an important parameter and should be included in the model equations to improve the fits to the observed data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003eComparison of experimental results\u003c/h2\u003e \u003cp\u003eTo increase reliability in the presented data, and demonstrate further consistency with the isotherms, two comparisons between the kinetic data and isotherms were made. First, we related the kinetic steady state ΔDOC with the final isotherm values (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Four sets of data from the Langmuir isotherm experiments (Pinsonneault et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) were directly compared to the kinetic data: Jug Bay Fresh (JBF, 0 psu), Jug Bay Saline (JBS, 35 psu), Taskinas Fresh (TAF, 0 psu), and Taskinas Saline (TAS, 35 psu). For the low [DOC] values, there were no differences between the isotherm and kinetic data points (p\u0026thinsp;=\u0026thinsp;0.995, two-sample t-test). For the high [DOC] data points, the kinetic experiments\u0026rsquo; concentrated stock solution was, on average, 88.8 mg-DOC L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e higher than the stock solution for the high initial [DOC] conditions of the isotherm experiments, but extrapolated confidence intervals captured the net DOC exchange of the kinetic points.\u003c/p\u003e \u003cp\u003eSecond, we compared the \u003cem\u003eLangmuir\u003c/em\u003e k\u003csub\u003eads2\u003c/sub\u003e:k\u003csub\u003edes\u003c/sub\u003e ratio, an estimate of binding affinity, with the binding affinities (K) reported in Pinsonneault et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The difference between the kinetic and isotherm data for any treatments are within the standard errors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), showing a high degree of reproducibility of the adsorption-desorption characteristics of these marsh soils despite differing experimental and analytical approaches.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of binding affinities (L mg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of the Langmuir isotherm as obtained from fits to equilibrium results (Pinsonneault et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and inferred from the fits to kinetic data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJBF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJBS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTAS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ek\u003csub\u003eads2\u003c/sub\u003e:k\u003csub\u003edes\u003c/sub\u003e (kinetic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBinding affinity (isotherm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.035\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u0026thinsp;\u0026plusmn;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eKinetic isotherms\u003c/h2\u003e \u003cp\u003eSorption kinetics in tidal marsh soils can vary dramatically between sites and for different initial conditions as demonstrated by the striking differences reported here for soils from two sites in Chesapeake Bay. When exposed to high [DOC], Jug Bay soils adsorbed more than twice as much DOC as Taskinas soils under both saline and fresh conditions. Under fresh conditions, desorption occurred from both soils, but Taskinas released more DOC over time. These results are consistent with previous isotherm experiments (Pinsonneault et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) that revealed more net adsorption in the Jug Bay soils and more net desorption in the Taskinas soils. The mechanisms driving these differences could relate to soil biogeochemical properties, with Jug Bay soils having higher amounts of mineral oxides, higher surface areas, and lower concentrations of organic matter (Pondell \u0026amp; Canuel, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and Taskinas having higher amounts of organic matter, lower abundance of mineral oxides, and lower specific surface area. These soil characteristics change the number of exchange sites, and combined with a concentration gradient of DOC, could determine the direction of DOC sorption (Kaiser \u0026amp; Zech, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Kothawala \u0026amp; Moore, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kothawala et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Chen et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) states that there could be a difference in sorption patterns over longer time scales than the 24 hours reported here, suggesting that amount of \u0026ldquo;sorption cycles\u0026rdquo; will shift what happens with adsorption as the soils go from more pristine to covered in OM. It is important to note, however, that the soil used in this experiment \u0026ndash; taken from the upper 40 cm of the marsh sites \u0026ndash; can be assumed to have already undergone many sorption cycles based on age, exposure, and the fact that a lot of organic matter is released from the soils themselves when introduced to the low [DOC] standard. The important role of soil mineral content, organic matter content, and [DOC] in regulating the magnitude and direction of sorption in coastal wetlands, as in many other ecosystems (Shields et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Groeneveld et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pinsonneault et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), suggests that numerical models of DOC export can parameterize sorption processes from spatial databases of these characteristics similar to those used to model soil carbon stocks (Rovai et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Twilley et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSalinity, as expected, had a large role in sorption quantity. Pinsonneault et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) suggested that salinity most likely reduces DOC binding affinity and increases sorption capacity. Of these two effects, increased sorption capacity dominates even with the possible decreased binding affinity, as the presence of high salinity consistently reduced the net amount of desorption over time. When coupled with high [DOC], net adsorption of DOC more than doubled between fresh and saline treatments, with the extent of the interactions varying by site and [DOC]. Jug Bay soils were capable of adsorbing twice the amount of DOC in the saline treatment relative to the fresh treatment, and Taskinas soils switched from net desorption to net adsorption at higher salinity. The salinity of the sites where the soils were collected is likely to be important for explaining these differences in sorption kinetics. As a brackish marsh, Taskinas soils may be less responsive to shifts in salinity than Jug Bay, a tidal freshwater marsh. Our data suggest there will be changes in DOC sorption to soil surfaces, and therefore to DOC exchange with estuaries, as tidal freshwater marshes become more saline with sea level rise. Conversely, disturbances such as impoundments, increased river discharge, or increased precipitation intensity may cause tidal marshes to freshen (Portnoy, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Kroeger et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), in which case soils similar to those at Taskinas could be susceptible to desorption under low salinity conditions (Reay \u0026amp; Moore, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese are the first reported kinetic results from tidal marsh ecosystems, highlighting the importance of the influence of salinity and organic matter content on sorption processes, and thus potential retention of carbon in marsh soils. We found that high initial DOC concentration and high salinity primarily drove the observed reductions in desorption. The magnitudes differed between the two locations, but the patterns remained the same. This indicates the speed and direction of the net sorption process can be estimated based on initial conditions, while soil characteristics can modulate the magnitude of these processes. The influences of short- and long-term salinity changes on marsh systems have been studied in the last few decades due to concerns about the impacts of increased storm frequency and intensity, more severe droughts, and salt intrusion due to sea-level rise (SLR; Kirwan \u0026amp; Megonigal, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Armitage et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Charles et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Spivak et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our kinetic incubations, with a maximum of 24-hour exposure of soils to higher salinity levels, represent the short-term effect of a change in salinity, but nonetheless provide vital information on how both freshwater and brackish marsh soils may respond to rapid changes in salinity and DOC concentration. This is directly relevant to storm-induced salinity changes, as the 24-hour timeline of the experiments captures the typical duration of storm surges (several hours to a couple days), and also shows how quickly these interactions would occur at the lowest elevations of the marsh by the creek edges (Danielson et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Leonardi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere have been recent results suggesting the opposite effect of salinity on sorption, reporting less adsorption with higher salinity (Tomaszewski et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We can most likely attribute these opposing results for this particular study, however, on several key differences in experiment design, such as: 1) lower initial DOC concentration (40 mg DOM L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e as compared with 280 mg DOC L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); 2) compositional differences in the soil compounds (specific Fe compound versus the complex marsh soil samples in this study); and 3) high levels of competition with other adsorbed ions that would likely limit adsorption extent in Tomaszewski et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). More importantly, Tomaszewski et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found similar results in terms of speed and drivers - our sorption kinetic incubations reveal that sorption responses can be fast and responsive to different sets of initial conditions, and that both the speed and magnitude of change can vary strongly between soils with different geochemical characteristics. The studies also agree that preferential adsorption of high-molecular weight compounds occurs, and carbon compound fractionation happened over time to lead to different sorption patterns (both of which will be reported in a forthcoming manuscript).\u003c/p\u003e \u003cp\u003eAlthough there were rapid DOC oscillations within minutes of the start of the incubation, the net DOC sorption processes were consistent and predictable under ideal conditions. Since the net DOC processes reached equilibrium within 24 hours and reacted similarly to the initial conditions across all incubations, it should be possible to model these processes as a function of just a few variables. The chemistry that drives the adsorption and desorption is complicated (Kleber et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), but salinity, [DOC] gradient between the soil and water, and the number and availability of adsorption sites (correlated with mineral concentrations) largely control DOC flux due to sorption. The addition of these processes to models such as the SFM (Di Toro, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) allows improved simulation of DOC fluxes from marshes with different soil characteristics and over a wide range of salinities (Morrissette, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eKinetic models\u003c/h2\u003e \u003cp\u003eA suite of simplified zero-dimensional models was constructed to simulate the above experiments and calculate the adsorption and desorption rates. These models omitted several aspects of the SFM that were not relevant to the closed-environment laboratory experiments, such as oxygen, diffusion, biological activity, etc. Additionally, unlike the SFM, DOC in these models was not partitioned by lability levels which means that the derived rate parameters cannot necessarily be directly applied. Nonetheless, the zero-dimensional models provided important insights into the magnitude of these rate parameters and how they might change over time and space. As more information emerges on different classes of organic carbon through higher resolution techniques like ICP-MS, HRMS, etc, these models can be updated to provide more complex descriptions of dynamics.\u003c/p\u003e \u003cp\u003eAll versions of the models started with two ordinary differential equations that could be solved analytically (Herman, 2018). The solution to these equations with constant rate parameters produced a simple non-linear exponential decay or saturation response that could simulate adsorption and desorption over time, however, the model fitted with a single set of rate constants did not always capture the DOC mass in solution at steady state for both low and high initial conditions. Comparison of the different simplified model versions revealed which mathematical expressions of sorption best reproduced the experimental observations. When all the time points were included, the time-dependent model always gave the best fits to the data (results not shown; see Supplemental), revealing the necessity of changing the rate constants to capture rapid initial variation in DOC concentrations. Tuning the \u003cem\u003eLinear\u003c/em\u003e and \u003cem\u003eLangmuir\u003c/em\u003e models to the full data sets produced poor fits that gave too much weight to the first 3\u0026ndash;10 minutes of the incubations (results not shown). Thus, in cases where very short-term variation (on the order of minutes) is important, these models may be inadequate when they are applied with constant rate parameters. On the other hand, when the models were fitted to the data without the initial oscillating time points, the performance of the \u003cem\u003eLinear\u003c/em\u003e and \u003cem\u003eLangmuir\u003c/em\u003e models improved, with better fits resulting from the \u003cem\u003eLangmuir\u003c/em\u003e model, which showed that the equations with saturation are best suited for modeling DOC sorption processes over longer time scales.\u003c/p\u003e \u003cp\u003eMost of the ΔDOC curves in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveal an initial spike of net desorption followed by net adsorption. There are four possible explanations for this apparent reversal in sorption processes: 1) immediate dissolution of precipitated non-adsorbed freeze-dried DOC followed by adsorption of this DOC on the soils; 2) different rates of adsorption and desorption, with desorption occurring faster and therefore dominating the sorption processes early in the incubation, and adsorption occurring more slowly and therefore having greater influence later in the incubation; 3) competing influences of the initial conditions, with the lack of salt providing an environment more conducive to desorption, but the very high [DOC] ultimately overwhelming the system and forcing the DOC to adsorb onto the soils; and/or 4) preferential sorption of separate DOC fractions onto the soils, replacing and subsequently releasing previously sorbed DOC into solution. Assuming that there was DOC present in the pore water at time of core extraction, rewetting (scenario 1) could have added up to 2 mg L\u003csup\u003e-1\u003c/sup\u003e (Jug Bay) or 4 mg L\u003csup\u003e-1\u003c/sup\u003e (Taskinas) to the solution. This suggests that re-dissolution was \u0026lt;\u0026thinsp;10% of the initial peak (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). So, this first possibility seems unlikely given our estimates of residual DOC in the sampled core, while the other three processes may have been occurring in tandem. Inferences about the fourth mechanism can be made by considering the contributions of two different DOC fractions, colored and non-colored DOC pools, to the sorption process at each time point. As reported by Morrissette (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Neale et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) for the incubation conditions described here and in Pinsonneault et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), there is preferential adsorption of colored, non-native organic carbon from the solutions of Great Dismal Swamp DOC and preferential desorption of non-colored, native organic carbon from the soil pools. This indicates that an interesting, competing set of dynamics between adsorption and desorption of colored and non-colored DOC pools is occurring during the incubations. Sorption has also been shown to fractionate DOC incubated with other types of sorbants (desert soils, Fe minerals, marine sediment) in terms of its molecular composition, hydrophobicity, and isotopic composition (Avneri-Katz et al., 2017; Tomaszewski et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hauksson et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Fractionation study results and their kinetics are forthcoming in Morrissette et al. (\u003cem\u003ein prep\u003c/em\u003e), and not discussed here due to manuscript length.\u003c/p\u003e \u003cp\u003eIt should also be noted that the Langmuir model consistently and drastically improved the fits to the incubation data over the longer time scales when compared to the linear model. This indicates that the potential for saturation of adsorption was present, and that the experiments could not be accurately simulated without accounting for saturation. Even though DOC concentrations in the water overlying soils in these marshes are much lower -- recent values report an average DOC concentration of 5\u0026ndash;6 mg L\u003csup\u003e-1\u003c/sup\u003e across four years of sampling at Taskinas Marsh (Knobloch et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and a maximum of 6 mg L\u003csup\u003e-1\u003c/sup\u003e at Jug Bay (Logozzo et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) -- Pinsonneault et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found evidence of potential saturation in the majority of the isotherm curves, especially under fresh conditions. It therefore seems most appropriate to use the saturated \u003cem\u003eLangmuir\u003c/em\u003e equations with constant rate coefficients in the SFM. This is especially true given that SFM models are often applied at annual and multi-annual time scales.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003eSalinity and DOC concentration had large influences on the DOC sorption processes of two tidal marsh soils, with synergistic effects. It is important to understand these impacts because tidal marshes are experiencing changes in both characteristics, on tidal to decadal time scales. For example, with sea level rise introducing higher salinity to tidal marshes and higher inundation levels in marshes (Webb et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Beckett et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Spivak et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), ionic perturbation of soils could increase bulk DOC sorption capacity, possibly preferentially releasing specific types of DOC compounds (e.g., noncolored; results forthcoming). In contrast, increased precipitation and freshwater runoff may decrease salinity in some cases. These competing influences could lead to rapid sorption interactions that influence organic carbon export to adjacent estuaries and oceans.\u003c/p\u003e \u003cp\u003eThis study was designed to isolate specific sorption processes on organic carbon concentrations while controlling for other factors that are relevant in situ. For example, incubations in a closed system do not allow for lateral export by horizontal flow and are therefore more representative of vertical DOC exchange by diffusion between marsh soils and soil porewater during an inundation event. But vertical diffusion is important to a marsh-wide DOC export budget because most influence of seawater comes from that tidal inundation over the soils (Guimond \u0026amp; Tamborski, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, groundwater export has been found to be much smaller than other pathways of exchange (Yelverton \u0026amp; Hackney, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Czapla et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Indeed, most studies emphasize the need to focus on processes at the soil surface-water column interface (French \u0026amp; Stoddart, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Goni \u0026amp; Gardner, 2003). This study therefore focuses on what is, arguably, the dominant pathway of DOC export and thus captures the most relevant effects of changes in porewater DOC concentration and salinity on marsh-water DOC exchange.\u003c/p\u003e \u003cp\u003eThe models\u0026rsquo; fits to the data provide a wide range of soil sorption responses under different initial conditions and salinity levels that produce a wide range of wetland sorption reaction rates. This leads to the question of how these rates should be used to inform SFMs. One thing was clear in our study: the sorption rates derived were 2 to 100 times faster than those that have been applied in previous SFM studies (Morrissette, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, when these faster rates were used in the SFM they gave very different simulation results that may be more consistent with DOC fluxes that are observed in marsh systems (Morrissette, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These results demonstrate that sorption processes should be included in all sediment flux models that track soil carbon flux to properly capture the fast reactions that can occur, especially if they are being used to simulate fluxes in response to perturbations. Inclusion of additional flux processes improves the confidence in abiotic transport of dissolved organic carbon and provides a better understanding of the temporal variability of tidal marsh DOC fluxes and budgets and their biogeochemical controls.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by National Science Foundation Grant DEB-1556556; NASA Grant NNX14AP06G; NSF-LTREB Program support of the Global Change Research Wetland (DEB-0950080, DEB-1457100, DEB-1557009); and the Smithsonian Environmental Research Center. This paper represents University of Maryland Center for Environmental Science Contribution No. 6300.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Hannah Morrissette, Patrick Neale, and Andrew Pinsonneault. The first draft of the manuscript was written by Hannah Morrissette and Patrick Neale, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eAuthors would like to thank the staff of the Jug Bay Wetlands Sanctuary, the York River State Park, the Virginia Institute of Marine Science (VIMS) Eastern Shore Laboratory, and the Great Dismal Swamp National Wildlife Refuge for their support and assistance. Specifically, we thank Michael Gonsior (University of Maryland Center for Environmental Science, Chesapeake Biological Laboratory), and Andrew Peresta (Smithsonian Environmental Research Center) for their assistance in the field and laboratory.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe datasets generated during and/or analyzed during the current study are available in the GitHub repository \u0026ldquo;Kinetic-Sorption-Incubation-Models\u0026rdquo;, with the model code and input files is available here: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://https://github.com/hannah-morrissette/Kinetic-Sorption-Incubation-Models\u003c/span\u003e\u003cspan address=\"https://https://github.com/hannah-morrissette/Kinetic-Sorption-Incubation-Models\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArmitage AR, Weaver CA, Kominoski JS, Pennings SC (2019) Resistant to Hurricane Effects Varies Among Wetland Vegetation Types in the Marsh-Mangrove Ecotone. 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Estuar Coastal Shelf Sci 22(2):255\u0026ndash;267. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0272-7714(86)90116-2\u003c/span\u003e\u003cspan address=\"10.1016/0272-7714(86)90116-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"wetlands","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wela","sideBox":"Learn more about [Wetlands](https://www.springer.com/journal/13157)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/wela/default.aspx","title":"Wetlands","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"sorption, kinetics, dissolved organic matter, tidal marsh, Langmuir model","lastPublishedDoi":"10.21203/rs.3.rs-3813404/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3813404/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSorption processes at the soil-water interface are observed to be rapid and dominant pathways of dissolved organic carbon (DOC) exchange. However, kinetics data for sorption are sparse, and non-existent for temperate tidal marshes. In this study, sorption rate kinetics experiments were designed to constrain new formulations of a sediment flux model coded to include explicit sorption between soil organic carbon and DOC pools. Batch incubations for marsh soil samples from Taskinas Creek (VA, USA) and Jug Bay Wetlands Sanctuary (MD, USA) were performed anaerobically under four sets of initial conditions: permutations of two salinities (0 psu, 35 psu) and two DOC concentrations (0 mg L\u003csup\u003e-1\u003c/sup\u003e, 275 mg L\u003csup\u003e-1\u003c/sup\u003e). Rates were measured at seven time points over 24 hours. These results are the first DOC sorption kinetics data for tidal marsh soils, revealing that 76% of total sorption occurred within 15 minutes. The results also revealed higher capacity for adsorption under high DOC concentrations and salinity, and vice versa, with differences in magnitude between soil types. Numerical models simulating processes from these experiments provided a range of rates by fitting linear first order and non-linear ordinary differential equations to the kinetic change in DOC concentration curves over time. The output suggested that introducing a saturation coefficient improved model fits across all cases. These results provide a deeper understanding of the biogeochemical controls on sorption kinetics and suggest that it is crucial to incorporate sorption processes into sediment flux models to accurately represent DOC fluxes from tidal marshes.\u003c/p\u003e","manuscriptTitle":"Wetland soil characteristics influence the kinetics of dissolved organic carbon sorption","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-18 15:29:15","doi":"10.21203/rs.3.rs-3813404/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-01-19T16:04:54+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-16T01:42:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Wetlands","date":"2024-01-08T17:10:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-28T05:46:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Wetlands","date":"2023-12-27T08:22:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"wetlands","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wela","sideBox":"Learn more about [Wetlands](https://www.springer.com/journal/13157)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/wela/default.aspx","title":"Wetlands","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"31337d61-fee3-4744-a29f-82ee52a292db","owner":[],"postedDate":"January 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-07-20T00:27:26+00:00","versionOfRecord":{"articleIdentity":"rs-3813404","link":"https://doi.org/10.1007/s13157-024-01835-2","journal":{"identity":"wetlands","isVorOnly":false,"title":"Wetlands"},"publishedOn":"2024-07-19 00:27:26","publishedOnDateReadable":"July 19th, 2024"},"versionCreatedAt":"2024-01-18 15:29:15","video":"","vorDoi":"10.1007/s13157-024-01835-2","vorDoiUrl":"https://doi.org/10.1007/s13157-024-01835-2","workflowStages":[]},"version":"v1","identity":"rs-3813404","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3813404","identity":"rs-3813404","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}